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Memetic Algorithm for the Generalized Asymmetric Traveling Salesman Problem

  • Gregory Gutin
  • Daniel Karapetyan
  • Natalio Krasnogor
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 129)

Abstract

The generalized traveling salesman problem (GTSP) is an extension of the well-known traveling salesman problem. In GTSP, we are given a partition of cities into groups and we are required to find a minimum length tour that includes exactly one city from each group. The aim of this paper is to present a new memetic algorithm for GTSP which clearly outperforms the state-of-the-art memetic algorithm of Snyder and Daskin [21] with respect to the quality of solutions. Computational experiments conducted to compare the two heuristics also show that our improvements come at a cost of longer running times, but the running times still remain within reasonable bounds (at most a few minutes). While the Snyder-Daskin memetic algorithm is designed only for the Symmetric GTSP, our algorithm can solve both symmetric and asymmetric instances. Unlike the Snyder-Daskin heuristic, we use a simple machine learning approach as well.

Keywords

Local Search Travel Salesman Problem Near Neighbor Previous Generation Memetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Gregory Gutin
    • 1
  • Daniel Karapetyan
    • 1
  • Natalio Krasnogor
    • 2
  1. 1.Department of Computer Science, Royal HollowayUniversity of LondonEghamUK
  2. 2.Automatic Scheduling and Planning group, School of Computer Science and I.T.University of NottinghamNottinghamUK

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